US7734661B2 - Method and apparatus for accessing multidimensional data - Google Patents
Method and apparatus for accessing multidimensional data Download PDFInfo
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- US7734661B2 US7734661B2 US10/915,570 US91557004A US7734661B2 US 7734661 B2 US7734661 B2 US 7734661B2 US 91557004 A US91557004 A US 91557004A US 7734661 B2 US7734661 B2 US 7734661B2
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/283—Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
Abstract
Description
Definitions |
Cell: | A location in a multidimensional database. A cell is a |
tuple of members. | |
Dimension: | A set of hierarchically related members. |
Grid: | A 2 dimensional representation of a multidimensional |
space. A single cell on a grid represents a single cell of | |
data in multidimensional space. The rows and columns | |
of a grid represent members of a dimension. | |
Dimensions not appearing in either row or column of a | |
grid are fixed to a single (offsheet) member. | |
Member: | A unique position on a dimension that includes in itself |
or points to data. | |
OLAP: | On-Line Analytical Processing. A category of |
applications and technologies that allow the collection, | |
storage, manipulation and investigation of | |
multidimensional data. | |
OLAP Server: | An application that provides OLAP functionality over a |
multidimensional database. | |
Outline: | The set of all dimensions in a multidimensional |
database. | |
re-ordering the structure of the one or more of selected sub-trees by either merging the selected one or more sub-trees, or re-ordering the dimensional hierarchy of the one or more selected sub-trees so that fewer branches of the index tree are required to be accessed to access the selected cells.
-
- 1. The multidimensional logical access model structure's purpose is to capture the target domain's logical structure. Its purpose is to define the presentation of data, not query optimization.
- 2. The data storage system's purpose is to arrange data to best suit the way in which it is being accessed. Its purpose is performance, not presentation.
-
- 1. A party wishing to insert or delete a cell must obtain write locks on all the affected index table rows and on the affected data blob(s). Relational databases have highly evolved transaction management. The relational database that is used in the preferred embodiment for the underlying storage for the index provides this behaviour.
- 2. A party wishing to return or update an existing cell only need obtain a lock on the data blob in which the cell resides. This behavior derives from (1), in which it is implied that no one else can update or delete the cell in question while there is a lock upon its data blob (even if there are not locks on the index entries that point to this data blob).
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- 1. Before accessing the hierarchical index, search through the data blobs that have already been retrieved (and implicitly locked). If the sought cell is found, return or update the cell data (no index access being required).
- 2. If the sought cell cannot be found in the data blobs that have already been retrieved and locked, look up the sought cell in the hierarchical index. Retain locks on all referenced index and data blobs until the current transaction ends.
-
- 1. Reduces the cost of random access within related sub-trees.
- 2. Decreases the potential for concurrent operations within the sub-trees due to holding locks over a larger region of the multidimensional space
-
- Height: The number of dimensions to interleave. This parameter is in the range (0, DimCount), where DimCount is the number of dimensions in the multidimensional data model. A height value of zero produces a non-interleaved tree, whereas a value of DimCount produces a tree that is completely interleaved into one level of nodes. We have found that useful values of height are typically those that cover only the lowest two or three dimensions.
- Width: The number of members within one dimension to be interleaved into the same relational database record (i.e. Blob in the example described). This parameter is in the range (2 . . . MemberCount), where MemberCount is the number of members in the dimension. Useful values for width are typically in the range (5 . . . 20).
-
- 1. Determine the set of fixed dimensions. These are the dimensions in the tree from the root down to localRoot, including localRoot.
- 2. Collate all cell addresses from fetch/update/insert queries that accessed cells that exist (or would exist, in the case of missing cells) in this sub-tree.
- 3. Perform a member frequency analysis of the cell addresses' non-fixed dimensions i.e. for each non-fixed dimension, count the number of distinct members encountered over the set of cell addresses. By way of example, this might look like:
Number of | |||
Non-fixed dim | |
||
4 | 12 | ||
3 | 1246 | ||
5 | 1 | ||
-
- Produce a list of the non-fixed dimensions, sorted into non-decreasing frequency of member change i.e. take the above results and sort the non-fixed dimensions by the number of distinct members encountered in each (least changing to most changing).
- For the above, this would look like:
- Non-fixed dim:
- 5
- 4
- 3
- 4. Rebuild the index sub-tree beneath localRoot, ordering the non-fixed dimensions according to the list generated in 3. Do not change the order of any two dimensions unless their frequencies of member change differ by a proportion larger than REORDERING_THRESHOLD.
Cell Data Optimization
-
- 1. When a new cell is created, it inserts that cell into a data blob such that it is likely to cohabit with other cells that will be accessed along with it.
- 2. Reorganization of existing cells within data blobs in order to provide optimal performance and concurrency for typical queries.
2. Define a function ƒ(p0, p1) to return a continuous value in the range (0, 1), where a value of 1 indicates points p0 and p1 are 100% likely to be accessed together, and 0 indicates points p0 and p1 are 0% likely to be accessed together.
3. Define a container to be a group in which Smax points may reside, where Smax is a non-zero positive integer. The number of containers allowed is unlimited.
-
- Reactive: Organize points such that they will be grouped optimally for historical access. This strategy aims to improve the performance of access that occurs repeatedly.
- Pre-emptive: Organize points such that they will be grouped optimally for access that has not yet occurred but is deemed likely to occur. This strategy aims to improve the performance of potential access that is very similar to, but distinct from, historic access.
-
- 1. The cells required for each “typical” query will usually reside in a very small number of cell data blobs compared with the number of cells. When the cost of accessing each cell data blob is high, this property maximizes performance by minimizing the number of cell data blob I/O requests.
- 2. Cells that are not required for a typical query are unlikely to reside in the cell data blobs accessed by the query. In a multi user system where each cell data blob can be accessed by only one participant at a time, this property maximizes potential concurrency.
-
- Macroscopic optimization is pre-emptive, and occurs when the index dimension re-ordering algorithm described above acts upon an index sub-tree. If dimension order was changed within the sub-tree, all cells within the sub-tree are then re-organized within data blobs to minimize the cost of a hypothetical depth-first traversal on the new index sub-tree hierarchy.
- Microscopic optimization is reactive, and can occur at any time. This optimization reorganizes cells within data blobs to minimize the cost of recurring access.
-
- 1. Allocate a new cell data blob Bnew.
- 2. Perform a depth-first traversal of cells starting from localRoot. Move each cell Ccurrent from its current cell data blob Bold into Bnew until BTC(Bnew, Ccurrent) returns true. When BTC returns true, store the current Bnew, and then allocate another Bnew. Whenever Bold becomes empty, delete it.
For each intersected cell Ci in Cintersected do | ||
{ | ||
Determine Cimax, the cell Ctest in Cisolated for which | ||
AdjacencyLevel(Ci, Ctest) is maximized. | ||
Move Ci into Cimax's blob group. | ||
} | ||
if(Cost0 − Cost1) / Cost0 > RELOCATION_THRESHOLD | ||
{ | ||
Relocate cells as proposed. | ||
} | ||
Claims (17)
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